Although pyrazinamide (PZA) is a key component of first- and second-line tuberculosis treatment regimens, there is no gold standard to determine PZA resistance. Approximately 50% of multidrug-resistant tuberculosis (MDR-TB) and over 90% of extensively drug-resistant tuberculosis (XDR-TB) strains are also PZA resistant. pncA sequencing is the endorsed test to evaluate PZA susceptibility. However, molecular methods have limitations for their wide application. In this study, we standardized and evaluated a new method, MODS-Wayne, to determine PZA resistance. MODS-Wayne is based on the detection of pyrazinoic acid, the hydrolysis product of PZA, directly in the supernatant of sputum cultures by detecting a color change following the addition of 10% ferrous ammonium sulfate. Using a PZA concentration of 800 µg/ml, sensitivity and specificity were evaluated at three different periods of incubation  using a composite reference standard . MODS-Wayne was able to detect PZA resistance, with a sensitivity and specificity of 92.7% and 99.3%, respectively, at reading 3. MODS-Wayne had an agreement of 93.8% and a kappa index of 0.79 compared to the classic Wayne test, an agreement of 95.3% and kappa index of 0.86 compared to MGIT-PZA, and an agreement of 96.9% and kappa index of 0.90 compared to pncA sequencing. In conclusion, MODS-Wayne is a simple, fast, accurate, and inexpensive approach to detect PZA resistance, making this an attractive assay especially for low-resource countries, where TB is a major public health problem.Although pyrazinamide (PZA) is a key component of first- and second-line tuberculosis treatment regimens, there is no gold standard to determine PZA resistance. Approximately 50% of multidrug-resistant tuberculosis (MDR-TB) and over 90% of extensively drug-resistant tuberculosis (XDR-TB) strains are also PZA resistant.  POA is then expelled by an efflux system into the acidic environment (pH 5.5) where it is protonated. This reenters the bacteria and releases the proton into the cytosol. Repetition of this cycle results in the accumulation of intracellular POA plus acidification of the cytoplasm, which is lethal to the mycobacteria  is not routinely performed due to notorious technical limitations. However, there is evidence that the empirical use of PZA in patients with PZA resistance is associated with higher mortality rates , and thein vitro . Currentin vitro . For exain vitro –19. Moren vitro ––22.pncA, which encodes PZase. These mutations often impair PZase activity and are the main cause of PZA resistance , but this assay also suffers from technical restrictions.More recently, a quantitative variant of the Wayne test has been reported  in whichpara-aminosalicylic acid, amikacin, ofloxacin, moxifloxacin, and streptomycin) (The microscopic-observation drug susceptibility (MODS) assay can accurately diagnose TB and MDR-TB in liquid culture medium after 5 to 21 days , 33. It 32–tomycin) , but suspncA sequencing, and the classic Wayne test to estimate sensitivity and specificity.Here, we present for the first time a colorimetric-qualitative adaptation of the MODS assay to determine PZA resistance directly from sputum samples. Because of the lack of a reference method test, the results of this assay have been compared to a composite reference standard , 38 compFor this study, 193 sputum samples were selected from a larger study conducted in 2015 to 2016. The remaining samples were obtained from TB patients enrolled in the National TB Strategy treatment program at the Hospital Nacional Dos de Mayo, Lima, Peru, and from the Regional Tuberculosis Reference Laboratory, Callao, Lima, Peru. All sputum samples selected for this study were positive for TB according to the MODS assay. Given that MDR-TB isolates have a 50% chance of also being resistant to PZA , 29, in N-acetyl-l-cysteine-sodium hydroxide (NALC-NaOH) by vortexing and then incubated at room temperature for 15 min. Next, 10 ml phosphate buffer was added to neutralize the solution. The solution was centrifuged at 3,000 × g for 15 min. The pellet was resuspended in 15 ml Middlebrook 7H9 culture medium  with oleic acid-albumin-dextrose-catalase  and polymixin-B-amphotericin-B-nalidixic-acid-trimethoprim-azlocillin  enrichment.Sputum samples were decontaminated according to the standardized MODS protocol . BrieflyM. tuberculosis was observed in either or both of the control wells , 0.5 µM primer P1  and primer P6  (Taq polymerase (Thermo Scientific). The PCR cycling parameters were set to one cycle of 94°C for 4 min, followed by 30 cycles of 94°C for 45 s, 58°C for 60 s, and 72°C for 60 s, with a final step of 94°C for 5 min. The 720-bp amplification products were sequenced using the same primers that were used for PCR. The presence of mutations in both the pncA gene and the putative promoter was evaluated by pairwise sequence alignment with the nucleotide sequence of the M. tuberculosis H37Rv reference strain (NCBI RefSeq accession no. NC_000962.3). Identified mutations were compared with those reported in the Tuberculosis Drug Resistance Mutation Database (For escribed . PCR wasof pncA) , and 0.0Database .M. tuberculosis culture was inoculated onto Dubos culture medium. The tubes were incubated at 37°C for 7 days. For each test round, positive (H37Rv) and negative (DM097) controls were included. For the test reading, 1 ml 1% SAF  was added, and the tubes were inspected for the presence of a pink band. Where this was not seen, the tubes were incubated at 4°C for an additional 4 h and then read again, and this was reported as the final result; the presence of a pink band indicated a susceptible strain, and the absence of color indicated a resistant strain.The classic Wayne test was performed as described previously . BrieflyIn order to explore a potential effect of the bacillary load in sputum with the MODS-Wayne assay, each sample was evaluated by smear microscopy using Ziehl-Neelsen staining. The procedure was performed and the slide interpreted according to standard techniques .M. tuberculosis isolates in culture. Unlike traditional PZA susceptibility tests, MODS-Wayne does not require an acidic pH because the goal of the assay is to detect the production of POA from PZA metabolism and not to evaluate PZA bacterial killing. The neutral pH permits the POA to accumulate in the supernatant where it can be detected using SAF.A variant of the MODS assay (MODS-Wayne) was standardized to determine PZA resistance directly from decontaminated sputum samples. This variant utilizes a Middlebrook 7H9 culture broth enriched with OADC and PANTA (pH 6.8) that favors the growth of Each decontaminated sample was cultured in 6 wells in a 24-well plate. Nine hundred microliters of the decontaminated sample was seeded per well. Three wells corresponded to control wells, and the other 3 wells were designated for PZA addition. As the test is performed using decontaminated sample, it is possible that the number of viable bacilli, or bacillus viability, could be reduced by stress during the decontamination process; in order to increase the number of bacilli and/or to improve the viability of stressed bacteria, the plate was first incubated at 37°C with no drug. The plate reading commenced on day 5 and was then performed every 2 days until day 21, according to the standard MODS protocol . When atAgreement between the dichotomous MODS-Wayne results and the composite reference standard for each of the three drug incubation periods was estimated using the Kappa statistic in a 2-by-2 contingency table. The potential effect of bacillary load or level of growth on the MODS-Wayne results was evaluated using Fisher’s exact test. For results with a positive association, a proportion test was performed to evaluate significant differences. The sensitivity and specificity  of the MODS-Wayne assay compared to those of the consensus reference test were estimated for each drug incubation period. McNemar’s test was used to determine significant differences between sensitivities and specificities among MODS-Wayne results. All statistical tests were reported with 95% confidence intervals and were performed using Stata version 14.0.Sputum samples were obtained from 193 patients. The descriptive statistics for the study population are shown in Of the 193 sputum samples collected, 101 (52.3%) were pansusceptible according to the MODS assay. Ninety-two samples (47.7%) had growth in the INH- and/or RIF-containing wells, indicating resistance; of these, 23 samples (11.9%) were monoresistant to isoniazid, 12 samples (6.2%) were monoresistant to rifampin, and 57 samples (29.5%) were resistant to both drugs .pncA gene in 51/193 samples (26.4%). Forty-seven (92.2%) of the mutations were single nucleotide polymorphisms, and four samples (2.1%) had a deletion in the pncA gene (nucleotides 376 to 389 or 456 to 466). SNPs were found at the putative promoter (A-11G), metal-binding site , and the active site (D8E). The PZA susceptibility profile was given according to data reported in the Tuberculosis Drug Resistance Mutation database   were PZA susceptible, and 46 samples (23.8%) were resistant. According to the classic Wayne test, 162 samples (83.9%) were PZA susceptible, and 31 samples (16.1%) were resistant. At least one mutation was detected in the database ; 154 sam[20.2%]) .Overall, according to the composite reference standard, 152 samples were reported as PZA susceptible (78.8%), and 41 samples were reported as PZA resistant (21.2%). Among these, 147 samples (76.2%) were sensitive according to all three tests used, and 29 samples (15.0%) were resistant according to all three tests . For MDRP = 0.06 for R1, P = 0.081 for R2, and P = 0.514 for R3).Acid-fast smear testing was positive for 182 samples. Of these samples, 6.0% (11/182) were reported as paucibacillary (less than 1 bacillus seen per microscopic field), 22.0% (40/182) as 1+, 33.5% (61/182) as 2+, and 38.5% (70/182) as 3+; 5.7% of the samples (11/193) were smear negative. A trend between bacillary load and the positivity of MODS-Wayne at the earlier time points was observed; however, this did not reach statistical significance  decreased with increasing reading day, from 104/193 (53.9%) to 39/193 (20.2%) . By R3, P = 0.045) and at R2 (P = 0.008) but not at R3 (P = 0.09).On mycobacterial culture, 50.3% of the total samples (97/193) had a high level of growth, 30.1% (58/193) had regular growth, 12.4% (24/193) had low growth, and 7.3% (14/193) had microcolonies only. Similarly to the MODS-Wayne results, the proportion of positive samples increased as reading day increased for each level of growth reported . A signiP < 0.0001), but no significant association was observed for samples with low growth or microcolonies . Conversely, low growth level and microcolonies were associated with a higher proportion of negative MODS-Wayne results at R1 , but no significant association was observed for samples with high and regular growth .High and regular growth levels were associated with a higher proportion of positivity in the MODS-Wayne assay at R2 (P > 0.05). However, the specificities showed a broader range, increasing from 57.9% to 99.3% (P < 0.05).Results from each reading day were compared to the composite reference standard independently. Sensitivities (true resistant percentage) in R1, R2, and R3 were similar, ranging from 92.7% to 97.6% (to 99.3%  and withP = 0.001). (Data from other time points not shown.) Ten isolates were PZA resistant according to the MODS-Wayne assay but PZA susceptible according to the classic Wayne test. When their genotypes were analyzed, nine isolates had mutations associated with PZA resistance , and one strain was wild type.As the MODS-Wayne and the classic Wayne assays obey the same principles, they were directly compared. At day 16, the sensitivity of MODS-Wayne was 93.5% and specificity was 93.8%, with an agreement and kappa index of 93.8% and 0.79, respectively  can accurately determine cificity . MODS-Wacificity .MODS-Wayne reported a median turnaround time of 18.5 days  for PZA-susceptible isolates and 21 days  for PZA-resistant isolates for results in R3. Although the MODS-Wayne turnaround time was higher than that reported for direct MGIT-PZA , Demers et al.  reportedpncA sequencing), we found that around half of the selected MDR-TB strains were PZA resistant (30/57 samples [52.6%]), but few of the selected pansusceptible strains were PZA resistant (4/101 samples [4.0%]). These findings are in agreement with rates reported by other studies (The use of a composite standard where no good single standard exists has been described . Using omples 52.%), but f studies .pncA that are close to the active site (Q10P and Q10R) or affect directly the metal-binding site (H51R and H57R), both of which are associated with PZA resistance  by detecting POA, the enzymatic product of the PZase. In this case, a mutation database is not required to interpret the results. Six of 7 isolates harboring K48T maintained PZase activity and were reported as positive by MODS-Wayne and PZA susceptible by MGIT-PZA. K48T is an example of a mutation that is not associated with PZA resistance. More studies are necessary to understand the real effect of some mutations that do not impair PZase activity on PZA susceptibility.Although MODS-Wayne does not evaluate activity . Not allM. tuberculosis in culture and its capacity to produce a detectable amount of POA. Our results showed that the level of growth influenced the time to positivity on MODS-Wayne, and there was also a trend in association of the bacillary load on smear microscopy with time to results, although this did not reach statistical significance. We hypothesized that a low number of PZA-susceptible bacilli can lead to a negative result in the early reading days, which can be considered false PZA resistance. This could be mitigated by using a longer duration of culture prior to the addition of PZA to the assay, which would increase the number of PZA-susceptible bacilli and give a positive MODS-Wayne result.As a phenotypic test, MODS-Wayne depends on the growth of pncA sequencing). The MODS-Wayne assay and MGIT-PZA had an agreement and kappa index of 95.3% and 0.86, respectively. The sensitivity and specificity of the MODS-Wayne assay were optimal in R3 ; although sensitivity was slightly higher in R2 (97.6%), our assumption is that longer culture duration may permit a more accurate discrimination of the PZA-resistant from the PZA-susceptible isolates. This will need to be confirmed in future studies.Our data show that the accuracy of MODS-Wayne (92% sensitivity and 99.3% specificity) is comparable to that of other tests for PZA resistance detection (MGIT, classic Wayne, and Finally, it is important to highlight that the MODS-Wayne assay lends itself to incorporation into the standard MODS assay. This would generate a single unique test that would be able to diagnose TB, MDR-TB, XDR-TB, and PZA resistance from the same sputum sample with high accuracy and in a relatively short time.in vitro. MODS-Wayne overcomes these difficulties and also has the advantages of low cost and being easy to perform and interpret. Thus, the MODS-Wayne assay offers the real possibility of introducing routine PZA susceptibility testing in resource-constrained and/or high-TB-burden settings. This would represent a major advance in the treatment of TB patients worldwide.In conclusion, here, we describe the MODS-Wayne test, a modification of the standard MODS method that is able to determine PZA susceptibility with high accuracy and in a relatively short time. Although DST for most first- and second-line TB drugs is available, PZA susceptibility testing is not routinely performed despite widespread use of the drug because of well-described difficulties in testing its activity


Moreover, the extent application of this simple assay was successfully demonstrated in tap water samples with high reliability and applicability, indicating remarkable application in real samples.A simple and dual modal (colorimetric and fluorescent) sensor for organophosphate pesticides with high sensitivity and selectivity using adenosine triphosphate (ATP)- and rhodamine B-modified gold nanoparticles (RB-AuNPs), was successfully fabricated. This detection for ethoprophos afforded colorimetric and fluorescence imaging changes visualization. The quantitative determination was linearly proportional to the amounts of ethoprophos in the range of a micromolar scale (4.0–15.0 µM). The limit of detection for ethoprophos was as low as 37.0 nM at 3σ/ Pesticides have been widely used worldwide to protect crops from insects and increase the quantity and quality of agricultural products ,2,3. PesMultiple methods to assay OPs have been developed, including gas chromatography-mass spectrometry, liquid chromatography, enzyme inhibition, enzyme-linked immunosorbent assay (ELISA), and electrochemical analysis ,19,20,21Gold nanoparticles (AuNPs) possess unique optical properties related to their size, surrounding environment, and dispersion state ,23. AuNPChlorpyrifos (CP), ethoprophos (EP), profenofos (PF), trichlorfon (TC), omethoate (OT), monocrotophos (MC), isocarbophos (IC), and malathion (MT) were purchased from Honeywell Fluka . Adenosine 5′-triphosphate disodium salt hydrate (ATP) was obtained from Sigma-Aldrich . Gold(III) chloride trihydrate (49.0% Au) and sodium citrate tribasic dihydrate were purchased from J&K Scientific . All commercial chemicals were used directly without any purification.The color changes caused by AuNP aggregation were measured by ultraviolet-visible light spectroscopy absorption (400–900 nm) using a Shimadzu 2600 spectrophotometer . Because the AuNP solution was highly concentrated, we double-diluted the solution and set the corresponding absorption band to 520 nm. The fluorescence intensity of the spectra was recorded at a range of 540–700 nm, operating at an excitation wavelength of 520 nm using a Hitachi F7000 fluorescence spectrophotometer . The weak fluorescence intensity of rhodamine B-AuNP (RB-AuNP) solutions at approximately 565 nm indicated that the fluorescence of the rhodamine B molecules was completely quenched by the AuNPs, confirming that nearly all rhodamine B molecules were adsorbed onto the surfaces of the AuNPs. The excitation and emission slit widths were both 10 nm. Fluorescence intensities were captured using an inverted fluorescence microscope . The diameter and morphology of AuNPs were imaged by transmission electron microscope (Hitachi HT7700), and hydrodynamic size distribution was measured using a particle size analyzer .Stock solutions of the pesticides (10.0 mM) were prepared in absolute ethanol and used within one month. The pesticide stock solutions were diluted 10-fold with absolute ethanol, to obtain 1.0 mM solutions.The standard serial solution of ethoprophos was one milliliter of 1.0 mM ethoprophos solution transferred to a 50-mL volumetric flask and diluted with deionized water to a specific volume, then fully mixed. The final concentration was 20.0 μM. Aliquots  of the pre-prepared ethoprophos (20.0 μM) solution were transferred to 10.0-mL volumetric flasks, diluted with deionized water to a specific volume, and fully mixed. The corresponding concentrations of these serial solutions were 2.0, 4.0, 6.0, 8.0, 10.0, and 15.0 μM.ATP (10.0 mM) and rhodamine B (10.0 mM) were prepared in deionized water and used within one week. For lower concentrations of solutions used in later experiments, the ATP and rhodamine B stock solutions were diluted with deionized water and used within 8 h.3+ molar ratio according to the modification by Turkevich and Frens [The AuNPs used in this experiment were water-soluble and had diameters of approximately 13 nm, as determined by transmission electron microscopy. The sodium citrate solution preparation described by Hauser and Lynn was used to synthesize citrate-capped hydrophilic AuNPs . By varynd Frens , the dia4  was dissolved in 100.0 mL deionized water and heated to boiling. The HAuCl4 solution appeared yellow. Ten milliliters of trisodium citrate solution (38.8 mmol/L) were then added slowly to the HAuCl4 solution with stirring. Color changes accompanied the reaction, from yellow to dark blue to a final transparent wine-red. The solution was heated for another 10.0 min. After cooling to room temperature, the obtained aqueous citrate-capped AuNP solution was filtered through a 0.22-μm hydrophilic membrane to remove insoluble residues and large solid particles.HAuClAuNPs were protected by ATP before coating with rhodamine B. First, a stock solution of ATP  was added to the citrate-capped hydrophilic AuNPs solutions (50.0 mL). The mixed solutions were vortexed for 30.0 min at room temperature. Next, 1.5 mL rhodamine B solutions (1.0 μM) were slowly added to the ATP-protected AuNPs solutions and vortexed lightly in the dark at room temperature for 30.0 min. The resulting RB-AuNPs solutions were stored at 4.0 °C as stock solutions for later use. The particle size of the RB-AuNPs was approximately 15 nm, and the particles were mono-dispersed in the solution.Citrate-capped AuNPs were first double-diluted for this experiment. The pesticide solutions  were added to the double-diluted citrate-capped AuNPs solutions (5.0 mL). Ethanol (50.0 μL) was added to the citrate-capped AuNPs solution (5.0 mL) as a blank, which eliminated interference and improved the precision of analysis. The blank test was repeated for 12 replicates to calculate the limit of detection (LOD). The mixtures were vortexed for 1.0 min and kept in the dark for 15.0 min before being imaged, and the ultraviolet-visible light absorption spectra (400–900 nm) were collected. All measurements were repeated for at least three replicates.The experimental procedure and conditions for the citrate-capped AuNPs assay were repeated, replacing citrate-capped AuNPs with RB-AuNPs. The solutions were imaged and the ultraviolet-visible light absorption spectra (400–900 nm) were collected. All measurements were repeated for at least three replicates.k, where σ is the standard deviation of the 12 blank tests, and k is the slope of the linear fitting equation.The standard ethoprophos serial solutions (0.5–100.0 μM) were mixed with the RB-AuNPs in a ratio of 1:1 in ultramicro cells , vortexed for 20 s, and the ultraviolet-visible light absorption spectra (400–900 nm) were recorded every 1 min 30 times. The LOD was calculated as 3σ/A520) by setting the blank value as zero. The corresponding LOD was also adjusted by deducting the blank value. We found that ethoprophos, profenofos, and chlorpyrifos induced aggregation of citrate-capped AuNPs. This was confirmed by the AuNPs’ color changes from red to purple and blue. The ΔA520 was 0.809 , 0.399 , and 0.129 . The AuNP solutions did not change color in the presence of the other pesticides (1.0 mM) or ethanol. The ΔA520 for malathion could not be determined. The ΔA520 values of trichlorfon, omethoate, monocrotophos, isocarbophos, and malathion were determined to be much smaller than those of ethoprophos and profenofos  versus ethoprophos concentration was plotted  were very small, and the color of the AuNPs did not change. In addition, the competition experiments were studied by measuring ROA600/520 in the presence of ethoprophos (4.0 μM) and interfering analytes with a specific concentration (4.0 μM). Very few changes in ROA600/520 were observed in the combination of ethoprophos and interfering pesticides, indicating that this simple assay for ethoprophos could be applicable in the complex samples.To improve the detection specificity of AuNPs for OPs, we measured the effects of OPs on RB-AuNPs, which were capped with ATP on the surface. ATP has a greater affinity for AuNPs than citric acid, because both adenine and triphosphate show some binding to AuNPs. This binding affinity is ATP concentration-dependent. We envisioned that AuNPs capped with a specific concentration could show specific assays for OPs based on the displacement of ATP and OPs. To selectively assay for ethoprophos, an ATP concentration was optimized to be 1.2 mM (a detailed discussion is presented in the next section). As expected, only ethoprophos caused a color change in RB-AuNPs, from wine-red to a pink color within 15.0 min a, indicaoprophos c. The ab plotted d. These  plotted . ProfenoIt is well known that AuNPs quench fluorophore fluorescence almost completely. Here, rhodamine B molecules were tightly adsorbed to the surface of AuNPs via electrostatic attraction to ATP, which was confirmed by the negligible fluorescence intensity of the RB-AuNPs. The fluorescent results showed that RB-AuNPs had a strong, concentration-dependent fluorescence recovery response to ethoprophos  and a neA520 change in ultraviolet-visible light absorption declined markedly from 0.809 to 0.205, indicating that ATP enhanced AuNPs’ stability and reduced the tendency to aggregate. Citrate-capped AuNPs had slight absorption responses to the other pesticides, whereas RB-AuNPs were not responsive. These findings suggest that ATP is protective of AuNPs and reduces OP-induced aggregation.We found that after modification of the AuNPs with ATP, OP-induced aggregation of the NPs was inhibited to some extent. In the first experiment, using citrate-capped AuNPs, the OPs ethoprophos, profenofos, and chlorpyrifos caused notable aggregation of AuNPs, visualized by the color change from wine-red to dark blue after 15 min. Added ATP, however, replaced citric acid as the stabilizing agent, as ATP has a greater affinity to AuNPs ,38,39. RATP concentrations and the detection reaction time were determining factors in this assay. Changes in the absorption spectra and color of RB-AuNPs were negligible for chlorpyrifos, trichlorfon, omethoate, monocrotophos, isocarbophos, and malathion, even at a high concentration of 50.0 mM. In the case of profenofos, slight variations in the absorption spectra and color of the RB-AuNPs were observed at low ATP concentrations or short reaction times of detection, suggesting that it may interfere with detecting ethoprophos. By optimizing the amount of ATP, we further tested various concentration of ATP. The results showed that the response to ethoprophos was in the range of 0.8–1.4 mM. At low concentrations of ATP, the sensitivity of the assay for ethoprophos was high. However, the RB-AuNPs showed some response to profenofos. At high concentrations of ATP, the sensitivity of the assay for ethoprophos was low, as there was no response to profenofos. As a result, the RB-AuNPs showed some response to profenofos in the low concentrations, and it took a long time to assay ethoprophos in the high concentrations. Given specificity and quickness, 1.0–1.2 mM ATP was optimized as a final concentration range. This established a method for detecting ethoprophos exclusively. The detailed data are summarized in A520 of RB-AuNPs with 10.0 μM ethoprophos at various pH values (from 3.0 to 10.0). The ΔA520 of RB-AuNPs in the presence of ethoprophos was stable (around 0.8) under acidic and weakly basic conditions. However, it dramatically changed to 0.04 at pH 10.0, probably due to the decomposition of ethoprophos under basic conditions. These results suggest that this assay is practical and applicable under broad pH conditions.Usually, pH shows a great effect on AuNP-based colorimetric sensors. The pH effect on this assay was studied in detail. Sulfur-containing OPs can replace citric acid and lead to aggregation of AuNPs because of a strong Au-S binding tendency. ATP has high affinity for AuNPs, and can be considered a good stabilizing agent: ATP molecules adsorbed to the surface of RB-AuNPs can compete with sulfur-containing OPs, depending on the ATP concentration. Therefore, a specific assay for detecting sulfur-containing OPs could be refined by ATP concentration. Ethoprophos has a great affinity for AuNPs because it contains two sulfur atoms and forms stronger Au-S bonds than other OPs. It can demolish the frameworks of RB-AuNPs to cause nanoparticles aggregation, affording colorimetric change and fluorescence recovery . An assak. The quantitative range was 4.0–15.0 μM.We used various concentrations of ethoprophos (0–20.0 μM) to study the absorption spectra of RB-AuNPs, and found that the decrease of adsorption at 520 nm and the increase of adsorption at 600 nm correlated with ethoprophos concentration b. The vaEthoprophos is commonly used for nematode control, and off-target spraying pollutes the surrounding environment and sources of drinking water. We used our RB-AuNPs assay to detect ethoprophos in tap water, anticipating that the complex matrices in a real sample would not interfere with accurate detection. A certain amount of ethoprophos was sprayed into tap water, and the obtained solution was concentrated to a specific volume and applied to RB-AuNPs. In this study, a simple colorimetric and fluorometric assay for detecting ethoprophos with high sensitivity and specificity using RB-AuNPs was established. The assay relied on ligand replacement on the surface of AuNPs, resulting in AuNP aggregation. This aggregation, in turn, permitted colorimetric visualization and fluorescence enhancement. The linear range of detection was calculated at the submicromolar level, and the LOD was determined to be 37.0 nM. This simple method was successfully applied to detect ethoprophos in tap water with high reliability. It is believed that this simple assay can be useful in monitoring ethoprophos on-site, especially in combination with other methods such as lab-on-chip techniques.


Since the early 1970s, numerous behavioral studies have shown that self-generated touch feels less intense and less ticklish than the same touch applied externally. Computational motor control theories have suggested that cerebellar internal models predict the somatosensory consequences of our movements and that these predictions attenuate the perception of the actual touch. Despite this influential theoretical framework, little is known about the neural basis of this predictive attenuation. This is due to the limited number of neuroimaging studies, the presence of conflicting results about the role and the location of cerebellar activity, and the lack of behavioral measures accompanying the neural findings. Here, we combined psychophysics with fMRI to detect the neural processes underlying somatosensory attenuation in male and female healthy human participants. Activity in bilateral secondary somatosensory areas was attenuated when the touch was presented during a self-generated movement (self-generated touch) than in the absence of movement . An additional attenuation effect was observed in the cerebellum that is ipsilateral to the passive limb receiving the touch. Importantly, we further found that the degree of functional connectivity between the ipsilateral cerebellum and the contralateral primary and bilateral secondary somatosensory areas was linearly and positively related to the degree of behaviorally assessed attenuation; that is, the more participants perceptually attenuated their self-generated touches, the stronger this corticocerebellar coupling. Collectively, these results suggest that the ipsilateral cerebellum is fundamental in predicting self-generated touch and that this structure implements somatosensory attenuation via its functional connectivity with somatosensory areas.SIGNIFICANCE STATEMENT When we touch our hand with the other, the resulting sensation feels less intense than when another person or a machine touches our hand with the same intensity. Early computational motor control theories have proposed that the cerebellum predicts and cancels the sensory consequences of our movements; however, the neural correlates of this cancelation remain unknown. By means of fMRI, we show that the more participants attenuate the perception of their self-generated touch, the stronger the functional connectivity between the cerebellum and the somatosensory cortical areas. This provides conclusive evidence about the role of the cerebellum in predicting the sensory feedback of our movements and in attenuating the associated percepts via its connections to early somatosensory areas. Imagine a situation where your brain cannot differentiate the sensory signals that your body produces from signals that originate from events, objects, and actions produced by others in the surrounding environment. In that bizarre situation, the world would appear to constantly move each time you perform a saccade or change your gaze direction, you would continuously wonder whether somebody is talking to you each time you speak, and you would relentlessly tickle yourself each time you touched your own body. One of the strategies the brain uses to avoid such situations is to suppress the perception of self-generated information and thus to magnify its distinction from externally generated input; consequently, self-produced signals feel less intense than signals of identical intensity that are due to external causes . This isIn the somatosensory domain, several behavioral studies have shown that the sensations produced by one of our hands voluntarily touching the other hand are systematically attenuated. For example, participants rate a self-generated tactile stimulus on their hand as less intense (and less ticklish) than an external stimulus of the same intensity and frequency . SimilarComputational theories of motor control have suggested that sensory attenuation is a perceptual correlate of the brain's machinery for motor control. Specifically, it has been theorized that our brain uses internal forward models, probably implemented in the cerebellum , to predWhat is the neural basis of somatosensory attenuation? In contrast to the plethora of behavioral paradigms, neuroimaging studies of somatosensory attenuation have been scarce and have provided contradictory results about the brain correlates of the phenomenon. In their seminal study, An additional observation that remains unclear concerns the site of cerebellar activity detected by the previous neuroimaging studies. Given that the somatosensory attenuation is observed on the passive limb that is receiving the touch  and thatFinally, none of the abovementioned studies included a behavioral assessment of somatosensory attenuation. This is a critical limitation in any study that aims to isolate the neural processes that are specific to sensory attenuation. Although the abovementioned studies revealed a different cerebellar pattern between self-generated and externally generated touch conditions, this does not necessarily mean that the cerebellum is genuinely involved in the predictive attenuation of self-generated touch because no relationship with the behaviorally registered attenuation has been established. Indeed, if the cerebellum is involved in predictive attenuation, one would expect increased cerebellar interactions with somatosensory areas for individuals who show stronger behavioral attenuation, indicating that the flow of information between those areas reflects the extent to which participants perceive their touch as weaker than external touch. Nevertheless, to our knowledge, there is no study assessing somatosensory attenuation at both the neural and behavioral levels; therefore, the cerebellar contribution and the neural basis of the phenomenon remain unknown.To address the abovementioned issues, here we combined fMRI with a force-matching psychophysics task and used a larger sample of participants than those used in earlier studies. In addition to merely contrasting self-generated touch and externally generated touch, we further took advantage of previous observations that not all self-generated touches are attenuated to the same extent but mainly those that correspond to direct self-touch where the two body parts in question are perceived to be in physical contact . For exaWe hypothesized that the attenuation of self-generated touch applied on the left index finger would be related to activity in the left cerebellum, that is, ipsilateral to the passive limb, compared with the control conditions. Moreover, we predicted that the degree of functional connectivity between the cerebellum and somatosensory areas would predict the degree of behaviorally estimated somatosensory attenuation across participants. Our results provide support for both of these hypotheses, which collectively provide strong evidence that the cerebellum plays a critical role in the attenuation of self-generated touch through its connectivity with somatosensory cortical areas.After providing written informed consent, 30 naive participants  20–39 years of age participated in the study. Handedness was assessed using the Edinburgh Handedness Inventory . The sam0cmpress, 25cmpress), the participants reproduced the presented force by pressing their right index finger against a force sensor that was placed either on top of (but not in contact with) the probe  or at a 25 cm distance from the probe . This sensor controlled the force output of the lever with an ∼30 ms intrinsic delay. In the third slider condition, the participants moved the wiper of a 13 cm slide potentiometer with their right hand (C). As with the sensor, the slider controlled the force output on the participants' fingers. The slider was positioned so that its midline laid at 25 cm to the right of the participants' left index fingers. The lower limit of the slider (left extreme) corresponded to 0 N and the maximum (right extreme) to 5 N. Each trial started with the slider at 0 N. This slider condition is a classical control condition known to not involve somatosensory attenuation but used to assess basic somatosensory perception.The psychophysics task was performed ∼30 min after the end of the fMRI experiment; this was the time it took to walk with the participants back from the scanner  to the psychophysics lab (Karolinska Institute). In the behavioral session, participants performed the classic force-matching task . In eachhe probe A,B. Thisght hand C. As wit0cmpress, 25cmpress, and slider) consisted of 36 trials, with each level of the presented force  pseudorandomly presented 6 times. To control for any order effects, the order of the conditions was fully counterbalanced across participants. During all conditions, the participants wore headphones through which white noise was presented to preclude the possibility that any noise generated by the motor served as a cue for the task. Auditory “go” and “stop” signals notified participants when to start and stop reproducing the presented force. A mark on the wall served as the participants' fixation point. The forces applied by the motor (presented force) lasted 3 s, and participants had 3 s to reproduce the perceived force (matched force). The next force was presented ∼3 s after the end of the previous matched force. No feedback was provided to the participants concerning their performance.Each of the three experimental conditions  corresponding to two repetitions of two different force levels were missing for 1 participant in one experimental condition. For 2 different participants, one repetition of one force level was missing, and another was accidentally repeated in one of the three experimental conditions.0cmpress, 25cmpress, and slider) as factors was used to analyze the matched forces. Planned pairwise comparisons were performed using either paired t tests or paired Wilcoxon signed-rank tests, depending on whether the data satisfied normality assumptions.The psychophysics data were processed with Python (version 2.7.10) and analyzed using R (version 3.5.3). A repeated-measures ANOVA with the presented force level  and the condition . A total of 1460 functional volumes were collected for each participant (365 volumes per run). For the anatomical localization of activations, a high-resolution structural image containing 180 slices was acquired for each participant before the acquisition of the functional volumes .A). Their left index finger was in contact with a 3D-printed probe that contained a force sensor (same specifications as above) and that was controlled by a motor  through string-based transmission. The string was tensioned through a pulley system consisting of ceramic bearings, and the transmission was mounted over a wooden structure with 6 degrees of freedom. Participants had their right index finger next to a second force sensor that was also placed on the table, either on top of (but not in contact with) the probe on the left index finger or at a 25 cm distance from it (A). Sponges were used to support the participants' arms in a comfortable posture inside the scanner so that they could keep their hands and fingers relaxed. Participants were instructed to fixate on the fixation cross displayed though a mirror screen that was mounted on the head coil (B).The fMRI experiment always proceeded the force-matching task to keep participants blind to the experimental hypotheses. During the MRI session, participants laid comfortably in a supine position on the MRI scanner bed with their left hands placed palm-up on an MR-compatible plastic table A. Their  from it A. Spongeead coil B.mu metal and placed within a larger aluminum box. The motor box was placed inside the MRI room as far as possible from the scanner, and it was screwed to the hospital furniture for safety reasons. The motor cable was fitted with ferrite sleeves and passed through a hole to the control room where it was powered. Signal-to-fluctuation-noise ratio tests ensured that the presence of the motor in the room did not produce any degradation in the quality of the MR images.The DC motor controlling the lever was shielded inside a custom-made box made of 0cm, self-generated movement0cm, external touch0cm, rest0cm, self-generated touch25cm, self-generated movement25cm, external touch25cm, and rest25cm . The message “feel” indicated an externally applied force (2 N) on their left index finger . The message “press” instructed participants to press the sensor with their right index finger, as strongly as needed to increase the height of a red bar and make it reach a green line limit corresponding to 2 N (conditions: self-generated movement); no touch was felt on the left index finger in these conditions. The message “press&feel” prompted participants to press the sensor with their right index finger (2 N) so that the red bar reached the green line, but in this condition, the participants simultaneously felt their self-generated touch on their left index finger (conditions: self-generated touch). Finally, the message “relax” asked participants to relax their hands (conditions: rest).Participants received visual instructions about the task in each condition on a screen seen via a mirror B. The mehttp://www.fil.ion.ucl.ac.uk/spm). Images were realigned, unwarped, and slice-time corrected. Outlier volumes were detected using the Artifact Detection Tools using the option for liberal thresholds . Then, the images were simultaneously segmented into gray matter, white matter, and CSF and normalized into standard MNI space. As a final step, the images were spatially smoothed using an 8 mm FWHM Gaussian kernel. The structural images were also simultaneously segmented (into gray and white matter and CSF) and normalized to MNI space.Functional data were preprocessed using the CONN toolbox (version 18a)  in StatiCompCor) as it is implemented in the CONN toolbox. Five principal components from white matter, five principal components from CSF, 12 principal realignment components (six plus first order derivatives) and scrubbing parameters, together with two principal components per condition (the time series and its first derivative), were extracted and used as confounds. A bandpass filter  was applied, and the data were linearly detrended.For the functional connectivity analysis, data were further denoised using the component-based noise correction method , and we inserted two repeated factors with unequal variance: the movement of the right index finger and the touch on the left index finger. A second factorial model was created to assess the effect of distance on the attenuation of self-generated touch. For this model, we used the condition regressors of all movement conditions , and we inserted two repeated factors with unequal variance: the touch on the left index finger and the distance between the hands (0 or 25 cm).At the second level of analysis, random-effects group analyses were performed by entering the contrast images of the condition regressors from each subject into two complementary full-factorial models. The first factorial model tested for the attenuation of self-generated touch compared with externally generated touch. For this model, we used the four condition regressors that corresponded to a distance of 0 cm  > 0, was calculated to investigate the attenuation of self-generated touch compared with externally generated touch after factoring out the main effects of movement and touch. This contrast allows to study the attenuation of touch on the passive left index finger that was produced by the moving right index finger, but importantly without the concomitant effects of the movement of the right hand. Similarly, the Touch × Distance interaction, that is, (self-generated touch25cm + self-generated movement0cm − self-generated touch0cm − self-generated movement25cm) > 0, served to distinguish the attenuation of self-generated touch from a condition that involved the simultaneous presence of movement and touch but no robust somatosensory predictions. Both directions of interaction effects as well as all the main effects are reported for clarity and transparency.Contrasts of interest focused on the interaction effects of each factorial model. Specifically, the a priori hypotheses about cerebellar and somatosensory areas in the corresponding 2 × 2 interactions, we applied a correction for multiple comparisons in all statistical tests within such ROIs. Specifically, two cerebellar ROIs were defined as spheres centered around the cerebellar peak found in the study of x = 22, y = −58, z = −22) and its contralateral analog, that is, that derived by flipping the x coordinate . These coordinates were originally specified in MNI space ; therefore, they were not converted from Talairach space. Somatosensory ROIs were defined as spheres centered around the corrected or uncorrected primary and secondary somatosensory cortical peaks detected from the main effect of touch. Since our factorial designs were balanced, the main effects and interactions are orthogonal contrasts, ensuring no statistical inference bias and allowing us to use the main effects as functional localizers  > 0, which assesses connectivity increases during the self-generated touch condition compared with external touch after factoring out the main effects, and the Touch × Distance interaction, that is, (self-generated touch0cm + self-generated movement25cm − self-generated touch25cm − self-generated movement0cm) > 0, which assesses connectivity increases during the self-generated touch condition compared with the simultaneous presence of movement and touch after factoring out the main effects. Since we were interested in the attenuation of self-generated touch, we only assessed increases, and not decreases, in brain connectivity in the self-generated condition compared with control conditions.A seed-to-voxel analysis was conducted in the form of generalized psychophysiological interactions  using th0cmMovement × 0cmTouch interaction), we used the difference between the mean matched force in the 0cmpress condition and the mean matched force in the slider condition. Analogously, to investigate connectivity increases in the self-generated touch condition with respect to the simultaneous movement and touch condition (Touch × Distance interaction), we used the difference between the mean matched force in the 25cmpress condition and that in the 0cmpress condition. By doing so, the contrasts of brain activation were “aligned” with the behavioral contrasts, allowing a proper covariate analysis. We tested for connectivity changes both between somatosensory and cerebellar areas as well as between different areas within the cerebellum. Statistical maps were assessed using corrections for multiple comparisons, as described above.To identify connectivity changes that were specific to somatosensory attenuation, we used as a second-level covariate the participants' attenuation index as extracted from the force-matching task. For each participant, we calculated the difference between the mean force he/she exerted in the condition of interest and the force that he/she exerted in a reference condition, similar to our previous study . SpecifiD shows the participants' performance per condition and presented force level. A repeated-measures ANOVA revealed a significant main effect of condition  = 121, p < 0.001, η2 = 0.020), a significant main effect of the presented force level  = 414.3, p < 0.001, η2 = 0.521), and a significant interaction  = 15.23, p < 0.001, η2 = 0.017). Pairwise comparisons between the levels of the presented forces revealed significant differences for each pair , confirming that the participants clearly discriminated each presented force level.Figure 1D, E, the participants produced stronger forces when their hands were horizontally aligned (mean ± SD = 3.915 ± 0.752 N) than when they were spatially separated (mean ± SD = 3.255 ± 0.711 N) or when they used the slider to reproduce the forces (mean ± SD = 2.392 ± 0.357 N). Pairwise comparisons revealed significant differences between the 0cmpress and the 25cmpress conditions (t(27) = 8.63, p < 0.001, 95% CI = , Cohen's d = 1.631), between the 0cmpress and the slider conditions (t(27) = 13.57, p < 0.001, CI = , Cohen's d = 2.564), and between the 25cmpress and the slider conditions (t(27) = 8.43, p < 0.001, CI = , Cohen's d = 1.593) . Together, these findings replicate previous results indicating strong attenuation when the hands simulate direct contact and significantly reduced attenuation when the hands are spatially separated or when a slider is used to reproduce the force . By confirming this, we can ensure that any differences in the BOLD signals were not due to different levels of force being experienced in the different conditions . To thisF = 0.06, p = 0.808, η2 < 0.001), no effect of mode  = 0.47, p = 0.499, η2 = 0.007), and no significant interaction between them  = 0.05, p = 0.820, η2 < 0.001). A Bayesian repeated-measures ANOVA using JASP .With respect to the left index finger sensor, a repeated-measures ANOVA with the factors of distance (0 or 25 cm) and mode  revealed no significant effect of distance  = 0.10, p = 0.758, η2 < 0.001), no effect of mode  = 0.21, p = 0.653, η2 < 0.001), and no significant interaction between these factors  = 0.02, p = 0.880, η2 < 0.001). A Bayesian repeated-measures ANOVA revealed that the data were 81.98 times more likely to occur under the null model  compared with a model including these effects .With respect to the right index finger sensor, a repeated-measures ANOVA with the factors of distance (0 or 25 cm) and mode (self-generated movement or self-generated touch) revealed no significant effect of distance . The interaction term of such a model represents the difference between externally generated and self-generated touch, critically after factoring out activity that is due to the main effect of movement or the main effect of touch alone. This design further allowed a direct comparison between our data and the data of Fig. 3-1; Table 2-1). The main effect of tactile stimulation on the left index finger was associated with activations in the right parietal operculum (putative secondary somatosensory cortex [S2]) and the right and left supramarginal gyri in the inferior parietal lobule . Situated in the inferior parietal lobe, the supramarginal gyrus is part of the sensory association cortex and is involved in higher-order somatosensory processing  was also activated (Table 2-2).As expected, the main effect of moving the right index finger revealed widespread activity in several areas, including the left primary motor cortex (M1), dorsal (PMd), and ventral (PMv) premotor cortex, supplementary motor area, and putamen and the right cerebellum  were detected at the right supramarginal gyrus next to S2, the junction between the right superior temporal gyrus and supramarginal gyrus, the junction between the left parietal operculum (S2) and supramarginal gyrus, and the left supramarginal gyrus and the left cerebellum (lobule VI) . No significant peaks were detected in the right cerebellum, even at the uncorrected level of p < 0.001 .When testing the bule VI) ; Fig. 3;0cmMovement × 0cmTouch interaction, direction: self > external). Moreover, to isolate those connectivity changes that were specific to the somatosensory attenuation, we included the participants' behavioral attenuation as a covariate in the analysis . Importantly, we found that the more the participants attenuated their self-generated forces in the force-matching task, the more the right supramarginal gyrus increased its connectivity with the left cerebellum (p < 0.05 FWE-corrected) . Notably, when we removed the behavioral covariate, no voxels were detected in the cerebellum at the p < 0.001 uncorrected threshold, suggesting that the participants' attenuation index was critical for this increased cerebrocerebellar connectivity.To examine which regions were responsible for driving the suppression of activity in somatosensory areas when the touch was delivered in the context of movement (self-touch), we conducted a generalized psychophysical interaction analysis (gPPI) to look for voxels in the whole brain that increased their functional connectivity with the peak at the right supramarginal gyrus  during sp < 0.05 FWE-corrected), when the touch was self-generated compared with when it was externally generated . We further observed connectivity increases to other regions within the cerebellum: bilateral peaks at lobules VII/VIII increased their connectivity with the seed at lobule VI the more the participants attenuated their self-generated forces in the force-matching task ; these connectivity changes, however, did not survive corrections for multiple comparisons (p < 0.001 uncorrected threshold). When removing the participants' individual behavioral attenuation as a covariate from the analysis, no significant increases (p < 0.001 uncorrected) were detected in the cerebellar connectivity with the somatosensory areas under discussion, and the intracerebellar effects disappeared (Table 3-4), which suggests that the functional connectivity under discussion is specifically related to somatosensory attenuation.When the seed was placed in the left cerebellum, the gPPI analysis revealed increased cerebellar connectivity with both the left and right supramarginal gyri/parietal opercula (S2) and the right primary somatosensory cortex (S1) . This design controls for the effects described above and tests for neural attenuation of self-generated touch when the hands simulated direct contact  compared with when the hands were separated by 25 cm, leading to significantly reduced attenuation.The previous factorial design tested for the differential effects between self-generated and external touch, importantly after controlling for the main effects of movement and touch. However, it does not control for pure effects of bimanual actions involving the simultaneous presence of movement and touch, divided attention to the two hands and sense of agency, factors that could influence the BOLD signal in regions related to sensorimotor processing. Therefore, complementary to our previous analysis, we constructed a factorial design with the four self-generated conditions  and, at the uncorrected level p < 0.001, the right primary somatosensory cortex (S1) . The main effect of distance revealed activity in motor-related areas, including the right and left precentral gyrus (M1) and the cerebellum , probably reflecting the difference in the postures of the arms in the distance manipulation  compared with when the hands are separated by 25 cm revealed significant effects in the left cerebellum (lobules VIIa Crus I/VI). Critically, the left cerebellum showed a suppression of activation in the absence of distance  than in the presence of distance  (p < 0.05 FWE-corrected) . No activations were detected in the right cerebellar hemisphere at the p < 0.001 uncorrected level . Moreover, no active voxels were observed for the Touch × Distance interaction in the opposite direction (self-generated touch0cm > self-generated touch25cm) at the uncorrected level of p < 0.001.The important 0cmpress and 25cmpress conditions. We found one peak of activation at the right postcentral gyrus (S1) that increased its connectivity with the cerebellum when the touch was presented in the absence of distance than in the presence of distance   as a function of the behaviorally registered attenuation across participants. Similarly, we observed that, within the cerebellum, the more participants attenuated their self-generated forces, the stronger the connectivity between the seed at left lobule VI/VIIa and the anterior part of lobule VI bilaterally . However, none of these peaks survived corrections for multiple comparisons. Notably, when we removed the covariate, we no longer observed these cerebrocerebellar and intracerebellar effects .Next, we looked for connectivity changes between the left cerebellar peak  and somatosensory areas using a whole-brain gPPI analysis that included the participants' attenuation index as a behavioral covariate, defined here as the difference between the matched forces in the p < 0.05 FWE-corrected) .Finally, we constructed the full factorial model with all three factors , and we calculated the three-way interaction using all eight conditions. This interaction reflects the difference between external and self-generated touch when the hands simulate direct contact (0 cm) compared with when the hands are apart (25 cm), after factoring out the three main effects and all the two-way interactions. Consistent with the results from our two two-way interaction analyses described above, this three-way interaction revealed significant activity in the left cerebellum (lobule VI)  compared with touch of identical intensity that is presented in the absence of movement , replicating previous results  and consWhat does this functional corticocerebellar coupling represent? By keeping in mind that functional connectivity between two areas does not necessarily imply a causal relationship , one couFig. 3-3; Fig. 4-1; Fig. 5-7; Fig. 5-9; Fig. 5-12). Lobule VI is part of the primary sensorimotor body representation in the cerebellum, whereas lobules VII/VIII constitute the second sensorimotor representation  and/or posterior (VII/VIII) arm representations. It is quite noteworthy that findings in the literature support this view: Sensory attenuation has been proposed to be an effective mechanism serving self-other distinction . Our fin10.1523/JNEUROSCI.1732-19.2019.t2-1Table 2-1Activation peaks for the positive effect of movement. Peaks reflecting greater effects during movement of the right index finger compared to rest (0cm > No Movement0cmMovement). Only the peaks that survived the FWE correction (p < 0.05) are reported for spatial restrictions. Download Table 2-1, DOCX file10.1523/JNEUROSCI.1732-19.2019.t2-2Table 2-2Activation peaks for the positive effect of touch. Peaks reflecting greater effects during tactile stimulation of the left index finger compared to no stimulation (0cm > No Touch0cmTouch). Download Table 2-2, DOCX file10.1523/JNEUROSCI.1732-19.2019.t2-3Table 2-30cm -by- Touch0cmMovement interaction.Activation peaks for the  Peaks reflecting greater effects of touch when this is presented in the absence of movement  compared to when it is presented in the context of movement (self-generated) . Download Table 2-3, DOCX file10.1523/JNEUROSCI.1732-19.2019.t2-4Table 2-40cm -by- Touch0cmMovement interaction at the uncorrected statistical threshold of p < 0.005.Cerebellar activation peaks for the  Cerebellar peaks reflecting greater effects of touch when this is presented in the absence of movement  compared to when it is presented in the context of movement (self-generated)  at an uncorrected threshold of p < 0.005. Download Table 2-4, DOCX file10.1523/JNEUROSCI.1732-19.2019.t2-5Table 2-50cm -by- Touch0cmMovement interaction.Cerebellar activation peaks for the  Peaks reflecting greater effects of touch when this is presented in the absence of movement  compared to when it is presented in the context of movement (self-generated) . Download Table 2-5, DOCX file10.1523/JNEUROSCI.1732-19.2019.t2-6Table 2-60cm -by- Touch0cmMovement interaction.Activation peaks for the  Peaks reflecting greater effects of touch when this is presented in the context of movement (self-generated) compared to when it is presented in the absence of movement  . Visual activations were due to the visual instructions since the messages given in the self-generated conditions  were different and longer than the messages given in the externally generated conditions . Download Table 2-6, DOCX file10.1523/JNEUROSCI.1732-19.2019.t3-1Table 3-1Peaks that increased their connectivity with the right supramarginal gyrus as a function of behavioral attenuation. Peaks reflecting greater connectivity with the right supramarginal gyrus when the touch is presented in the context of movement (self-generated) compared to when it is presented in the absence of movement  in relation to force-matching task performance . Download Table 3-1, DOCX file10.1523/JNEUROSCI.1732-19.2019.t3-2Table 3-2Peaks that increased their connectivity with the left parietal operculum S2/SMG as a function of behavioral attenuation. Peaks reflecting greater connectivity with the left somatosensory seed when the touch is presented in the context of movement (self-generated) compared to when it is presented in the absence of movement  in relation to force-matching task performance . Download Table 3-2, DOCX file10.1523/JNEUROSCI.1732-19.2019.t3-3Table 3-3Peaks that increased their connectivity with the left cerebellum as a function of behavioral attenuation. Peaks reflecting greater connectivity with the left cerebellar seed when the touch is presented in the context of movement (self-generated) compared to when it is presented in the absence of movement  in relation to force-matching task performance . Download Table 3-3, DOCX file10.1523/JNEUROSCI.1732-19.2019.t3-4Table 3-4Peaks that increased their connectivity with the left cerebellum independently of the participants’ behavioral attenuation. Peaks reflecting greater connectivity with the left cerebellar seed when this is presented in the context of movement (self-generated) compared to when it is presented in the absence of movement  . Download Table 3-4, DOCX file10.1523/JNEUROSCI.1732-19.2019.f1-1Figure 1-1Line plots for the individual changes in matched forces of the participants during the three conditions of the force-matching task. (A) Individual changes between the 0cmpress condition and the 25cmpress condition. (B) Individual changes between the 0cmpress condition and the slider condition. Among all conditions, the 0cmpress condition yielded the strongest matched forces . Download Figure 1-1, TIF file10.1523/JNEUROSCI.1732-19.2019.f2-1Figure 2-1Force sensor recordings during the fMRI experiment. Recordings are displayed for the left (A) and right (B) index finger per distance and condition using raincloud plots . The individual points represent the raw data and the rain plots represent the data distribution. Summary statistics are represented with boxplots: the thick vertical line represents the median, and the boxes represent the interquartile ranges (IQR), the right whisker expands from the hinge to the largest value no further than 1.5 * IQR and the left whisker expands from the hinge to the smallest value at most 1.5 * IQR. In the externally generated touch condition the variability of the forces is much smaller compared to all other conditions since those forces were delivered by the motor and not by the participants. Download Figure 2-1, TIF file10.1523/JNEUROSCI.1732-19.2019.f3-1Figure 3-1Activations revealed by the positive effect of movement. Activations reflect greater effects during the two conditions that involved the pressing movement of the right index finger compared to the other two conditions that involved no movement (0cmMovement> 0cmNo Movement). Visual areas were also activated since the movement of the right index finger was visually guided. The activations have been rendered on the standard single subject 3D-volume provided with SPM. For visualization purposes, the activation maps are displayed at a threshold of p < 0.001 (uncorrected). Download Figure 3-1, TIF file10.1523/JNEUROSCI.1732-19.2019.f3-2Figure 3-2Activations revealed by the positive effect of touch. Activations reflect greater effects during the two conditions that involved stimulation of the left index finger compared to the two conditions that involved no stimulation (0cmTouch > 0cmNo Touch). (A) The activations have been rendered on the standard single subject 3D-volume provided with SPM. (B) Slice view of two peak coordinates surviving FWE corrections at the whole brain level, denoted by black circles and situated at the right parietal operculum and the left supramarginal gyrus. The activations have been overlaid on the average anatomical image of the participants. For visualization purposes, the activation maps are displayed at a threshold of p < 0.001 (uncorrected). Download Figure 3-2, TIF file10.1523/JNEUROSCI.1732-19.2019.f3-3Figure 3-30cm -by- Touch0cmMovement interaction at the uncorrected statistical threshold of p < 0.005.Cerebellar activation revealed by the  The activations have been overlaid onto a cerebellar flatmap. At this liberal statistical threshold, the peak location of the cerebellar clusters remained roughly the same. Download Figure 3-3, TIF file10.1523/JNEUROSCI.1732-19.2019.f4-1Figure 4-1Cerebellar peaks showing increased connectivity with the left or right somatosensory areas as a function of behavioral attenuation. Cerebellar peaks increasing their connectivity with (A) the right supramarginal gyrus or (B) the left parietal operculum/supramarginal gyrus overlaid onto a cerebellar flatmap. The seeds have been overlaid on the average anatomical image of the participants. The cerebellar activations have been overlaid onto a cerebellar flatmap. The maps are seen at an uncorrected threshold of p < 0.001. (A) Peaks in the right cerebellum did not survive corrections for multiple comparisons. It should be mentioned that when we removed the behavioral covariate, no voxels were detected in the cerebellum at p < 0.001 uncorrected, suggesting that the participants' attenuation index was critical for cerebrocerebellar functional connectivity. A very similar pattern was observed in the left cerebellum when we placed the seed at the peak of the left parietal operculum/supramarginal gyrus and again used the behavioral attenuation as a covariate (B), although the peaks did not survive corrections for multiple comparisons. As before, when the covariate was removed, no cerebellar peaks survived the p < 0.001 uncorrected statistical threshold. Download Figure 4-1, TIF file10.1523/JNEUROSCI.1732-19.2019.f4-2Figure 4-2Cerebellar peaks showing increased connectivity with the left cerebellar seed as a function of behavioral attenuation. (A) Bilateral cerebellar peaks at lobules VIIb/VIIIa increased their connectivity with the cerebellar seed at lobule VI . The peaks did not survive corrections for multiple comparisons (p < 0.001 uncorrected). The seed has been overlaid on the average anatomical image of the participants. The cerebellar activations have been overlaid onto a cerebellar flatmap.  Scatterplots showing the relationship between the connectivity increases for the peaks and the participants' behavioral attenuation as measured in the force-matching task. All activations are seen at p < 0.001 uncorrected for visualization purposes. Download Figure 4-2, TIF file10.1523/JNEUROSCI.1732-19.2019.f5-1Figure 5-1Activations revealed by the main effect of touch. Activations reflect greater effects during the two conditions that involved stimulation of the left index finger compared to the two conditions that involved no stimulation (Touch > No Touch). (A) The activations have been rendered on the standard single subject 3D-volume provided in SPM. (B) Slice view of the peak surviving FWE corrections at the whole brain level, indicated by the black circles and situated at the right parietal operculum. The activations have been overlaid on the average anatomical image of the participants. For visualization purposes, the activation maps are displayed at a threshold of p < 0.001 (uncorrected). Download Figure 5-1, TIF file10.1523/JNEUROSCI.1732-19.2019.f5-2Figure 5-2Activation peaks for the positive effect of touch. Peaks reflecting greater effects during tactile stimulation of the left index finger compared to no stimulation (Touch > No Touch). Download Figure 5-2, DOCX file10.1523/JNEUROSCI.1732-19.2019.f5-3Figure 5-3Activation revealed by the main effect of distance between the hands. (A) Activations reflect greater effects of having the hands horizontally aligned compared to having them spatially separated (0 cm > 25 cm). (B) Activations reflect greater effects of having the hands spatially separated than horizontally aligned (25 cm > 0 cm). The activations have been rendered on the standard single subject 3D-volume provided with SPM. For visualization purposes, we display activations at p < 0.001 uncorrected. Download Figure 5-3, TIF file10.1523/JNEUROSCI.1732-19.2019.f5-4Figure 5-4Activation peaks for the positive effect of distance. Peaks reflecting greater effects when the hands had no horizontal distance compared to when the hands were spatially separated (0 cm > 25 cm). We report a maximum of 3 peaks per cluster and only the peaks that survived FWE correction (p < 0.05) for spatial restrictions. Download Figure 5-4, DOCX file10.1523/JNEUROSCI.1732-19.2019.f5-5Figure 5-5Activation peaks for the negative effect of distance. Peaks reflecting greater effects when the hands were spatially separated compared to when the hands had no horizontal distance (25 cm > 0 cm). Download Figure 5-5, DOCX file10.1523/JNEUROSCI.1732-19.2019.f5-6Figure 5-6Touch -by- Distance interaction.Activation peaks for the  Peaks reflecting greater effects during the simultaneous presentation of movement and touch compared to the self-generated touch condition (Direction: 25 cm > 0 cm) at an uncorrected threshold of p < 0.001. Download Figure 5-6, DOCX file10.1523/JNEUROSCI.1732-19.2019.f5-7Figure 5-7Touch -by- Distance interaction (Direction: 25 cm > 0 cm) at an uncorrected threshold of p < 0.005.Cerebellar activation revealed by the  The activations have been overlaid onto a cerebellar flatmap. At this liberal statistical threshold, the peak location of the cerebellar clusters remained roughly the same. Download Figure 5-7, TIF file10.1523/JNEUROSCI.1732-19.2019.f5-8Figure 5-8Touch -by- Distance interaction at a lower statistical threshold.Cerebellar activation peaks for the  Peaks reflecting greater effects when the touch is presented in the context of a 25 cm hands' distance compared to when it is presented in the absence of distance at an uncorrected threshold of p < 0.005. Download Figure 5-8, DOCX file10.1523/JNEUROSCI.1732-19.2019.f5-9Figure 5-9Somatosensory and cerebellar peaks showing increased connectivity with the left cerebellum as a function of behavioral attenuation. (A) Slice views of the peak in the right primary somatosensory cortex. The peak is indicated by the black circles and did not survive corrections for multiple comparisons. The activations have been overlaid on the average anatomical image of the participants. (B) Scatterplot showing the relationship between the connectivity increase of the peaks in (A) and the participants' behavioral attenuation as measured in the force-matching task. (C) Location of the cerebellar peaks that increased connectivity with the left cerebellar seed overlaid into a cerebellar flatmap. The peaks did not survive corrections for multiple comparisons.  Scatterplots showing the relationship between the connectivity increases in the anterior part of lobules VI bilaterally and the participants' behavioral attenuation as measured in the force-matching task. All seeds have been overlaid on the average anatomical image of the participants. All activations are displayed at p < 0.005 uncorrected for visualization purposes. Download Figure 5-9, TIF file10.1523/JNEUROSCI.1732-19.2019.f5-10Figure 5-10Peaks that increased their connectivity with the left cerebellum as a function of behavioral attenuation at a lower statistical threshold. Peaks reflecting greater connectivity with the left cerebellar seed when the touch is presented in the absence of hands’ distance compared to when it is presented in the context of a 25 cm distance in relation to force-matching task performance  at an uncorrected threshold of p < 0.005. Download Figure 5-10, DOCX file10.1523/JNEUROSCI.1732-19.2019.f5-11Figure 5-11Peaks that increased their connectivity with the left cerebellum at a lower statistical threshold without the participants’ attenuation covariate. Peaks reflecting greater connectivity with the left cerebellar seed when the touch is presented in the absence of hands’ distance compared to when it is presented in the context of a 25 cm distance  at an uncorrected threshold of p < 0.005. Download Figure 5-11, DOCX file10.1523/JNEUROSCI.1732-19.2019.f5-12Figure 5-12Distance -by- Movement -by- Touch interaction.Cerebellar activation revealed by the  The activations have been overlaid onto a cerebellar flatmap. The peak denoted by the black circle survived FWE corrections. All activations are seen at p < 0.001 uncorrected for visualization purposes. Download Figure 5-12, TIF file


Metabolite identification for untargeted metabolomics is often hampered by the lack of experimentally collected reference spectra from tandem mass spectrometry (MS/MS). To circumvent this problem, Competitive Fragmentation Modeling-ID (CFM-ID) was developed to accurately predict electrospray ionization-MS/MS (ESI-MS/MS) spectra from chemical structures and to aid in compound identification via MS/MS spectral matching. While earlier versions of CFM-ID performed very well, CFM-ID’s performance for predicting the MS/MS spectra of certain classes of compounds, including many lipids, was quite poor. Furthermore, CFM-ID’s compound identification capabilities were limited because it did not use experimentally available MS/MS spectra nor did it exploit metadata in its spectral matching algorithm. Here, we describe significant improvements to CFM-ID’s performance and speed. These include (1) the implementation of a rule-based fragmentation approach for lipid MS/MS spectral prediction, which greatly improves the speed and accuracy of CFM-ID; (2) the inclusion of experimental MS/MS spectra and other metadata to enhance CFM-ID’s compound identification abilities; (3) the development of new scoring functions that improves CFM-ID’s accuracy by 21.1%; and (4) the implementation of a chemical classification algorithm that correctly classifies unknown chemicals (based on their MS/MS spectra) in >80% of the cases. This improved version called CFM-ID 3.0 is freely available as a web server. Its source code is also accessible online. Liquid chromatography (LC) coupled to mass spectrometry (MS) or tandem mass spectrometry (MS/MS) has become one of the leading techniques for compound identification in organic chemistry, natural product chemistry, and metabolomics ,2. In thBecause of the limited availability of many authentic chemical standards in most metabolomics labs, putative metabolite identification is more commonly performed . Putativn spectral tree  of an oe (MAGMA ). Still e  is an example of a software tool that uses hand-made fragmentation rules. Once the rules are implemented, this approach can be very fast, consistent, and accurate. However, a major disadvantage to this approach is that the design of fragmentation rules requires considerable expert curation. Furthermore, these rules cannot be applied to novel classes of molecules. For these reasons, much more emphasis has recently been put toward the implementation of computational combinatorial fragmentation approaches. Combinatorial fragmentation approaches iteratively cleave chemical bonds within a molecule in a combinatorial fashion, and use (or learn) penalty scores that favor the cleavage events that are most likely to occur at each step. Examples of tools that implement combinatorial fragmentation include CFM-ID ,11,12, MCFM-ID is a publicly available software tool and web server that can be used for MS/MS spectral prediction, MS/MS spectrum peak assignment, as well as MS-based compound identification. It implements a technique known as Competitive Fragmentation Modeling (CFM). CFM is a probabilistic generative modeling method that uses a customized cost function to take into account the structural composition of a molecule to predict its electrospray ESI-MS/MS spectrum. The original version of CFM-ID was used to generate a reference MS/MS spectral library of over 51,000 known compounds from the HMDB  and KEGGm/z values and heuristically modeled static relative abundances for fragment ions, the annotation of fragment ion peaks is limited to formulas and does not include actual structures. Moreover, LipidBlast predict consensus mass spectra, and does not distinguish between different collision energies. These are the kinds of output that are typically found with most in silico fragmenters, and these shortcomings have been addressed in this update to CFM-ID.For instance, one well-known limitation of CFM-ID is its very slow and relatively poor performance for predicting MS/MS spectra of lipids and other large “segmented” or modular metabolites. This is primarily due to the length of the fatty acids or attached head-group segments, leading to a combinatorial explosion of the possible fragments at each step of the in silico fragmentation process. As demonstrated by Kind et al. , who devhttp://cfmid3.wishartlab.com. Its source code is accessible at https://sourceforge.net/p/cfm-id/wiki/Home  and https://bitbucket.org/wishartlab/msrb-fragmenter/ (rule-based fragmentation tool).In addition to the incorporation of compound-specific fragmentation rules, it has also been shown that significant improvements in MS-based compound identification can be achieved by including metadata or other forms of external data in the spectral matching and scoring functions . In part5H14NO4P+ ion; neutral mass = 184.0733 Da) and the cyclic 1,2-cyclic phosphate diester . + precursor ions. The number of rules for each lipid class and the number of covered adduct types per lipid class are shown in https://bitbucket.org/wishartlab/msrb-fragmenter/.Our manual analysis of experimentally acquired lipid MS/MS spectra provided a basis for the generation of 344 unique fragmentation rules covering 21 lipid classes and 7 adducts, for a total of 50 combinations of lipid classes and adduct types. Each rule describes a chemical reaction that fragments the precursor molecule to generate a specific fragment. The structure and mass-to-charge ratio of the fragment can be easily computed based on the encoded pattern. For each lipid class, an ESI-MS/MS spectrum can be simulated by CFM-ID 3.0 at collision energies of 10, 20, and 40 eV. In general, almost all ESI-MS/MS spectra of lipids show similar fragmentation patterns with characteristic losses of the polar head group, and the acyl or alkyl chains, with relatively little fragmentation within the acyl or alkyl chains. For example, in choline-containing glycerophospholipids, the most commonly observed fragments include phosphocholine . Because of improvements in the spectral prediction performance, additions of new compounds, and the decision to add experimental spectra, the new CFM-ID 3.0 spectral library has been expanded by a factor of 2.6 over the original CFM-ID 2.0 spectral library (as of February 2019). In particular, the new library now contains a total of 167,547 computationally generated ESI-MS/MS spectra (generated via CFM-ID 3.0) from 108,972 compounds in the HMDB , KEGG  is about 23.75 ± 0.2 s. Clearly, the computational slow-down for lipid spectral calculation  is quite significant, which largely motivated us to develop a faster rule-based approach.+), with a collision energy of 40 eV.In terms of spectral prediction performance, the average spectral similarity score (measured using the dot product) between the experimental lipid ESI-MS/MS spectra (collected on a QTOF at multiple collision energies) and the CFM-ID 3.0-predicted ESI-MS/MS spectra was 0.85 ± 0.2. On the other hand, the average spectral similarity score between the CFM-ID 2.0-predicted ESI-MS/MS spectra and the experimental ESI-MS/MS spectra was 0.09 ± 0.1. This suggests that the accuracy of CFM-ID 3.0 for lipid spectral prediction is 11X better than that of CFM-ID 2.0, which is highly significant. It is worth mentioning that CFM-ID predicts ESI-MS/MS spectra at three different collision energies while other programs, such as LipidBlast, generate a consensus MS/MS spectrum that essentially merges the MS/MS spectra over all collision energies. Therefore, during our comparative analysis, only one LipidBlast-generated consensus ESI-MS/MS spectrum was used for each unique compound and compared against the experimental spectrum, independent of the energy level. The average spectral similarity score between the LipidBlast-predicted ESI-MS/MS spectra and the experimental ESI-MS/MS spectra was 0.34 ± 0.4. + adducts at 40 eV. For this example, the spectral similarity score was 0.88 when comparing the experimental ESI-MS/MS spectrum with the CFM-ID 3.0-predicted spectrum. Surprisingly, the dot product score was only 0.13 when compared with the LipidBlast-predicted ESI-MS/MS spectrum to the experimental ESI-MS/MS spectrum. −) ion mode at a collision energy of 40 eV. The measured spectral similarity scores between the experimental and the in silico-generated spectra are 0.10, 0.92, and 0.91 with CFM-ID 2.0  for every predicted peak. LipidBlast only provides the m/z values and intensities.As highlighted in A set of 1,000 compounds was used to train a new and improved scoring function for ESI-MS/MS-based compound identification see . This fuFor this assessment, the 208 challenge MS/MS spectra (corresponding to 185 distinct compounds) from the 2016 CASMI competition were used as queries. CFM-ID 3.0 was used to predict the chemical class of the query compound with the predicted class being the direct parent (according to ClassyFire −), and, if possible, from various MS instruments. This was used to help capture fluctuations or biases that can be introduced by the different parameters. A total of 533 experimental MS/MS spectra were collected for 16 standard lipids  from 15 lipid classes at various collision energies (10 to 60 eV), in both positive and negative mode using an AB Sciex QTrap 4000 MS instrument. For each lipid standard, an enhanced MS (EMS) scan was first collected to identify precursor ions with high abundance in either ionization mode. Enhanced product ion (EPI) scans were then collected for each precursor ion to generate the MS/MS spectra with different collision energy levels ranging from 10 to 60 eV, with the supervision of a mass spectrometry expert. For more information about the collection of spectra for the 16 standard lipids, see m/z list provided in the LIPID MAPS spectrum. In addition to the experimental spectra, the LipidBlast and FAHFA 26 libraries, as well as MassBank −) ionization modes, one for each of three collision energies . In order to significantly improve identification rates, the new CFM-ID library was updated as described below.The original reference spectral library in CFM-ID 2.0 contained unique computationally generated ESI-MS/MS spectra for ~51,000 compounds from the HMDB and KEGG databases. These in silico ESI-MS/MS spectra were computed in positive  online repository . As of FSince CFM-ID uses models trained on MS/MS spectral data sets that use specific collision energy and mass accuracy criteria, the HMDB, MoNA, and NIST spectra were further filtered to match these criteria. Specifically, experimental MS spectra were required to have a known ionization type, a known compound neutral mass, and to have been analyzed with high-resolution MS instruments  in the case of LC-MS spectra. After filtering, there were 72,678 usable experimental MS/MS spectra remaining from the MoNA dataset. These experimental MS/MS spectra were converted into the peak list format required for CFM-ID and uploaded into CFM-ID’s online spectral library. In addition, the complete library of experimental MS/MS spectra from the HMDB was obtained from our in-house repository, and filtered. Upon filtering, this library contained 1152 unique ESI-MS/MS spectra for 239 unique compounds.The original CFM-ID 2.0 database contained 102,153 unique computationally generated ESI-MS/MS spectra . Among the 102,153 ESI-MS/MS spectra, 36,746 were previously computed for 18,373 unique compounds belonging to the 21 lipid classes covered by the rule-based fragmenter, and transferred to the CFM-ID 3.0 database. The remaining 65,407 mass spectra computed by CFM-ID 2.0 were also moved to the CFM-ID 3.0 database. In total, ~36,900 spectra were generated for 18,438 lipids. To this database, another ~207,956 ESI-MS/MS spectra were computed for another ~80,000 lipids and 7288 other metabolites obtained from new versions of HMDB, DrugBank, and PhytoHub. These compounds were added to the CFM-ID 3.0 database. These predicted ESI-MS/MS spectra were generated for both positive and negative ion mode as well as at three different collision energies . In total, the CFM-ID 3.0 database now contains 310,109 computationally generated ESI-MS/MS spectra . If the experimental ESI-MS/MS spectra are added to this total, the CFM-ID 3.0 spectral database now contains a grand total of 393,158 ESI-MS/MS spectra.The results of the Critical Assessment of Small Molecular Identification (CASMI) 2016 contest showed that the integration of additional data  into the original scoring function for CFM-ID improved compound identification rates . This trTOTALS(C), CFM_ORIGS(C), CLASSS(C), and REFS(C) are the total score, the normalized spectral matching CFM-ID score, the normalized ClassyFire score, and the normalized reference score for candidate C, respectively. Each of the three scores are weighted by the coefficients aCFM_ORIG, aSCLAS, and aREF, respectively, where:The chemical classification score is based on the number of chemical categories to which a compound is assigned (by ClassyFire), relative to the total pool of chemical classes assigned to all candidate molecules. The chemical classification score was added to help re-rank or cluster structurally similar molecules (and MS spectra) closer together. Each of the three scores was normalized by dividing its computed score by the maximum score across the candidate list. The general formula for the total candidate score is:CFM_ORIG = 0.6, aSCLAS = 0.1, and aREF = 0.3.This scoring function was then iteratively optimized on a training data set to maximize its metabolite identification potential. In particular, the optimal set of coefficients was determined through a grid search using a manually selected set of 1000 spectral/compound identification tasks (for 1000 unique compounds ranging from drugs to lipids). Each of the selected molecules had one or more experimental spectra at one of three level energies , in addition to the predicted ESI-MS/MS spectra. The data set was divided into five equally sized subsets. Several models (with a unique combination of coefficients) were trained on 800 compounds (4/5 of the data set) and tested on the remaining 200 (1/5 of the data set). This process was repeated four more times, using a different test set of 200 compounds for each iteration. Experimental spectra were used as input for each identification test, and upon testing, only the best model was selected. A consensus model was built based on the five selected models, and further tested using a smaller test set. The final coefficient values for the ESI-MS/MS scoring function were aThis function was further refined to improve its performance and to deal with certain extreme or rare situations. In particular, we observed certain cases in which the spectral similarity between a query and a database match is so close that applying the citation and chemical classification scores causes such strong matches to be unfairly penalized. In order to prevent this, we implemented a 95% similarity threshold, above which only the original spectral similarity score is applied, and the citation and classification information is disregarded. Moreover, our training showed that the enormous discrepancy between citation counts  could negatively impact the identification rate. For instance, a compound that would be correctly identified using spectral similarity (with or without metadata) could be easily ranked much lower, in favor of a similar compound with a significantly high citation score. Thus, the citation count was adjusted to have a ceiling corresponding to 156 (2 × average citation count) for every compound that has over 156 citations.Three types of performance tests were conducted. The first assessed the performance of the lipid ESI-MS/MS spectral prediction method, the second assessed the performance of the new scoring function in exact compound identification, and the third assessed CFM-ID’s performance in identifying a compound’s correct chemical class. To test the lipid ESI-MS/MS spectral prediction method, a benchmark analysis was performed on 20 randomly chosen lipids from each of the 21 known lipid classes for which fragmentation rules were derived. The computation was performed on a 2.7 GHz Intel Core i5 running macOS with 16 GB (1867 MHz DDR3) of memory. A total of 120 ESI-MS/MS spectral predictions were generated for both CFM-ID 2.0 and CFM-ID 3.0 at 3 different energies and 2 different ionization modes with various adduct types. The average execution time was determined for each spectral prediction. In addition to the execution time comparison, an additional performance comparison was conducted to assess the quality of the predicted MS/MS spectra. For this task, a set of 5 experimental ESI-MS/MS spectra measured in positive ion mode, and 9 experimental ESI-MS/MS spectra measured in negative ion mode were selected. The selected spectra were measured under conditions that can be simulated by CFM-ID 3.0’s lipid fragmentation rules . For each experimental MS/MS spectrum, CFM-ID 2.0 and CFM-ID 3.0 were used to predict a corresponding MS/MS spectrum under the same conditions. The performance was assessed by measuring the average pairwise spectral similarity between experimental and predicted spectra using a standard dot product score as implemented in the OrgMassSpecR package . MoreoveThe ESI-MS/MS scoring function was tested on a set of 208 experimental ESI-MS/MS spectra (for 185 unique compounds) generated on a Q Exactive Plus Orbitrap (Thermo Scientific), and used for the CASMI 2016 contest (Category 3) . These sFor the third assessment, CFM-ID 3.0 was evaluated on its performance in chemical class prediction/identification. This particular task assessment was included because in many situations involving MS-based metabolomics or MS-based natural product identification, it may not be possible to identify the exact compound via MS/MS spectral matching. Therefore, the ability to use MS/MS spectra to reduce the candidate list and to predict the correct chemical class or correct chemical family for a given query spectrum or compound can be very valuable. In assessing the performance of CFM-ID’s chemical class prediction, the query compound was predicted to belong to the “direct parent” class of the highest-ranked candidate. In cases of a tie, the chemical class was predicted to be the most frequently occurring among all the direct and alternative parents among all the compounds with the highest score.We have shown that it is possible to substantially improve CFM-ID’s performance in both spectral prediction and compound identification tasks. This was achieved through a number of ways including (1) integrating a rule-based fragmentation approach that currently applies 344 manually curated rules to predict the ESI-MS/MS spectra for 21 classes of common, biologically important lipids, (2) modifying the structure of CFM-ID’s spectral database, and increasing its size by a factor of 2.6, (3) designing new scoring functions that take into account both compound citation frequency and chemical classification features of candidate molecules, and (4) implementing a chemical classification algorithm based on spectral similarity.In particular, the implementation of a rule-based approach for fragment ion prediction was shown to improve the speed by a factor of 200X and the accuracy of the lipid ESI-MS/MS spectra prediction by a factor of 10X. The success of using rule-based fragmentation patterns encoded in standard chemical representations  suggests that this concept could be successfully applied to other classes of modular molecules such as carnitines, polyphenols, terpenes, and carbohydrates. The construction and expansion of CFM-ID’s spectral library has also helped CFM-ID’s overall performance. The most recent spectral library has been expanded by a factor of 2.6 over the previously released spectral library. This expansion process is still ongoing, and we plan to include ~500,000 more compounds including drugs, lipids, environmental pollutants, phytochemicals, food compounds, as well as their predicted metabolites generated by BioTransformer ,19.The fields of in silico metabolomics and in silico mass spectrometry are rapidly evolving. Thanks to the many excellent ideas emerging in many labs around the world and the willingness of many researchers to share their code and their databases, it is likely that these fields will continue to grow and continue to inspire others to make MS spectral analysis, MS spectral prediction, and MS-based compound identification better, faster, and even more informative.